Information fusion reliability analysis for component survivability

Abstract

For many operational equipment systems, both reliability and survivability are measures of effectiveness over the performance of the individual mechanical and electrical parts. Many equipment parts can be modeled individually for their operational reliability performance due to physical constraints. Reliability has traditionally been assessed from physical attacks that result in failures; however, in real-world analysis, there are cases of non-physical attacks. A survivability analysis is an aggregate of the system-level operational sustainability over the reliability of all the components. With the information age, many equipment parts are "intelligent" that include sophisticated reasoning methods that are subject to non-physical cyber attacks. Developing a model that incorporates both the physical and non-physical attacks for reliability and survivability is important for determining system-level effectiveness. For sustained operations, we need to incorporate information fusion over physical and non-physical (e.g. cyber attacks) failures to determine a system's reliability and survivability. In this paper, we develop a method of fusing reliability estimates in both continuous (component model) and discrete analysis (component attack model) for a component survivability analysis.

One decade of the Data Fusion Information Group (DFIG) model

“…The group started with the Uncertainty Representation and Reasoning Evaluation Framework (URREF) [82,83]. While numerous examples have been presented over multimedia data; the intersection with the DFIG includes applications for LLIF [84], metric analysis [85], standards [86], and coordination with Bayesian networks [87]. Future developments would provide a bridge between LLIF and HLIF through semantic descriptions of uncertainty through an ontology.…”